Overcompliance in Point-Source Water Pollution

There are four categories of explanations for over-compliance. The project will attempt to determine the extent to which they contribute to observed emissions levels. The four explanations are: (i) the cost of reducing emissions is zero; (ii) plants are compensating for randomness in emissions; (iii) the production technology is not smooth; and (iv) there are non-regulatory benefits of over-complying. For example, if emissions are random, then plants may want to pollute below their limit, on average, to reduce the probability that they will accidentally violate. If this explanation for over-compliance is correct, then plants should choose lower mean emissions, relative to permitted levels, the higher is the emissions variance. Similar statistical tests are developed for the other over-compliance explanations.
Approach: The project explores these issues through empirical econometric analysis using the PCS data. We develop and test hypotheses based on each of the four explanations, concentrating on (ii) and (iii).
Expected Results: The explanations have greatly different implications for emissions regulation. For example, the way in which violators are identified and the penalties they are subject to will be an important determinant of emissions if emissions randomness is a major cause of over-compliance. On the other hand, non-regulatory influences, such as community pressure, will be largely unaffected by enforcement strategies. Because of these differences, the intended result is a better general understanding of why plants over-comply with relevant regulations. We hope to learn the specific features of the CleanWater Act that may have contributed to over-compliance. We would also like to know the extent (in a quantitative sense) to which non-regulatory influences have led to lower emissions. These non-regulatory influences could include community, market, and firm-governance pressures.

For the Year 2000Objective: The objectives of this research project are to investigate the factors that affect the quality of the wastewater that is discharged from point sources in the United States. There appears to be widespread overcompliance with some of the relevant regulations; in other words, the effluent is cleaner than it needs to be. This research project examines the nature of this overcompliance and tries to distinguish among the possible reasons. The implications for water pollution policy are discussed.

Progress Summary: This research examines point-source water pollution in the United States. All dischargers that report to NPDES were examined, although most of the data were from wastewater treatment plants. We examined monthly average biological oxygen demand (BOD) concentrations in wastewater, on a plant-by-plant basis, over an 8-year period from 1992 to 1999.

There appears to be substantial overcompliance with the relevant regulations. Each plant faces a limit (usually 30 mg/L) on the monthly average concentration of its effluent. In our data, average concentrations are far below this limit, often in the range of 6 mg/L. We call this "over-compliance." Let c be the ratio of the discharge concentration to the limit; this is the compliance ratio. Thus, in this example, c = 6/30, or 0.2. Whenever c < 1, the plant was overcomplying in that month. When c > 1, the plant is in violation. In much of the research, we looked at a plant's median compliance ratio where the median is taken over all months of data.

Previous understanding of overcompliance has been quite limited. A seminal work in this area (Harrington) used a theoretical model to explain why plants might be in compliance even when penalties for violation were low. However, his model did not allow plants to overcomply; plants could do no better than c = 1.

Randomness in U.S. Water Pollution

Many of the people we have talked to about this issue (primarily employees of the U.S. Environmental Protection Agency [EPA] and state regulators) speculated that the low levels of discharges, on average, are warranted by discharge randomness. Plants are believed to pollute below their permitted level, on average, to compensate for the possibility of an unexpectedly large discharge. Thus, the main thrust of this research has been on the role of randomness.

It is worth noting that almost no other economic studies have examined discharge randomness empirically. In Harrington's model, discharges were determinate (nonrandom). Randomness in his model entered through the probability that a pollution violation would be detected. McClelland and Horowitz, in an empirical study of pulp and paper plants, argued that because EPA was supposedly focusing on long-term violators, whereas most of the randomness is on a much shorter scale, there would be little randomness in the relevant measure of discharges.

Our investigation of the data shows that discharges exhibit considerable randomness on a month-to-month basis. This randomness was much greater than we expected. If EPA were to focus on monthly discharges, or discharges over several months, then this randomness could be important in decision-making by the permit holders.

Plants face regulations that govern both monthly average concentrations and daily maximum concentrations. Of course, there is even greater randomness from day to day than at the monthly level. We believe monthly concentration is the correct measure on which to focus for three related reasons:

EPA guidelines specify that plants should design their wastewater treatment systems to be in compliance with the monthly permits (that is, not the daily permits).

EPA does not collect data on daily maximum concentrations. This makes sense on the basis of reason 1. Of course, this point also necessitates that we focus on monthly rather than daily compliance).

The daily limits are set higher than the monthly limits to accommodate randomness in BOD levels. In other words, the relationship between daily and monthly limits is designed so that a plant in compliance at the monthly level will typically be in compliance at the daily level.

Findings

This long introduction on discharge randomness is necessary to understand our research findings. Our research has focused on the role of randomness. This role is more important and more interesting than we originally thought.

We used PCS data to construct plant-level measures of discharge randomness (i.e., plant-level variances.) We have data for as many as 108 months; we included in our analysis all plants with at least 15 months of data, not necessarily consecutive. In our regressions, we weighted observations by the number of months of data used in constructing the randomness measure.

Some variability in plant discharges is not random and must be removed to measure the true random component. We used two methods for removing nonrandom components. We used a CUSUM test to identify plants whose discharges underwent a statistically significant structural change. Such structural changes could be due to capital changes at the plant or changes in plant management. This is not the kind of variability whose role we seek to understand. We then analyzed the set of 764 plants for which there was no structural change (Sample A).

We also used information about permit renewals to analyze discharge behavior within a permit cycle. This yielded a set of 628 plants (Sample B).

For each plant, we used the median compliance rate and the standard deviation to construct the predicted probability of a single month's violation, using the assumption that discharges are distributed log normal. These predicted probabilities are quite low. The median probability of violation is less than 1 percent.

To see how low this probability is, note that it means a violation is expected to occur only once every 100 months. For multiple violations, which is a more likely target of EPA, the probability is even lower. The probability of four violations in a 6-month period is less than one in a million. EPA may impose a penalty of $3 million for a 4-month violation. The expected penalty for a 4-month violation is 45 cents.

Next, we analyzed the factors that affect the probabilities of violations. For each plant, we constructed the variable y = ln(median c)/ ln(c), which is proportional to the probability of a violation. Our final report describes in greater detail why we chose this formula. It is essentially the ratio of mean discharges over the standard deviation. For some plants, the permitted concentration changes over time, but for most plants it is constant. Therefore, for most plants, changes in c are identical to changes in discharges. When the permitted concentration changes, it is desirable to include this in calculating the relevant compliance and variability measures.

We found that: (1) there is no statistical difference in the behavior of EPA- and state-regulated plants; (2) manufacturing plants overcomply more than wastewater treatment plants; in other words, manufacturing plants have a lower predicted probability of violation; (3) larger plants overcomply more; and (4) community-level variables such as income or minority composition appear to play only a very minor role. All of these results are conditional on the plant being in Sample A or B. Samples A and B give essentially the same results.

To further understand the role of randomness, we also examined whether plants with higher discharge variability chose lower average discharges. This is the basic implication of the randomness argument. We regressed median compliance against the standard deviation and found a significant negative relationship, as the theory implies. This relationship has not been tested or reported before to our knowledge.

This research was conducted with Sushenjit Bandyopadhyay, a graduate student in the Economics Department at the University of Maryland. In our final report, we will discuss these issues, motivations, and arguments in greater detail.

Policy Implications. This role for discharge randomness seems to imply that making water pollution discharges tradeable (i.e., tradeable permits) would allow plants to hedge their randomness and therefore to increase average discharges. Of course, this claim must be treated with caution. It is merely a preliminary hypothesis. Because we find that plants are overcomplying, and not merely appearing to overcomply due to randomness, it is necessary to understand their behavior to understand how plants would behave if discharges were tradeable.

We find extremely low probabilities of violation. We believe-although this remains only speculation at this point-that many plant managers may not understand the implications of the magnitude of their discharge variability. Thus, they may not realize how much they are overcomplying.

Future Activities: The main body of the research has been completed. There remain many interesting issues to be pursued. However, the goals of the original research project have largely been met. We will prepare and submit the final report for this grant.

Presentations:

Horowitz JK. Overcompliance in point source water pollution. Presented at the AERE Workshop, La Jolla, CA, June 2001.

Horowitz JK. Overcompliance in point source water pollution. Presented at EPA's Beyond Compliance Workshop, June 2001.

For the year 2001

Objective:
The objective of this research project is to investigate the factors that affect discharges from point sources in the United States. There appears to be widespread overcompliance with the permits; in other words, discharges are cleaner than they need to be. This research project examines the nature of this overcompliance and tries to distinguish among the possible reasons. The implications for water pollution policy are discussed.

Progress Summary:
This research examines point-source water pollution in the United States. All dischargers that report to the National Pollutant Discharge Elimination System (NPDES) were examined, although most of the data were from wastewater treatment plants. Monthly average biological oxygen demand (BOD) concentrations in wastewater were examined, on a plant-by-plant basis, over an 8-year period from 1992 to 1999.

There appears to be substantial overcompliance with the NPDES permits. Each plant faces a limit (usually 30 mg/L) on the monthly average concentration of its discharges. In NPDES data, average concentrations are far below this limit, often in the range of 6 mg/L. Let c be the ratio of the discharge concentration to the limit; this is called the "compliance ratio." Thus, in this example, c = 6/30, or 0.2. Whenever c < 1, the plant was overcomplying in that month. When c > 1, the plant is in violation.

In the main research, a plant's median compliance ratio was examined where the median is taken over all months of data.

Regulators and plant managers claim that such low discharge levels are warranted by discharge randomness: plants are believed to pollute below their permitted level, on average, to compensate for the possibility of an unexpectedly large discharge. This view is widely shared but has received little empirical attention. The lack of confirming evidence leaves open the possibility that plans are over- or under-compensating for randomness, or perhaps responding to additional factors besides randomness. Thus, the main thrust of this research has focused on the role of discharge randomness.

Previous understanding of overcompliance has been quite limited. A seminal work in this area (Harrington) used a theoretical model to explain why plants might be in compliance even when penalties for violation were low. However, his model did not include randomness in discharges, and did not allow plants to overcomply; plants could do no better than c = 1. Randomness in Harrington's model entered through the probability that a pollution violation would be detected.

Only a few economic studies have examined discharge randomness empirically. In the paper closest to this study, Brannlund and Lofgren demonstrated that water pollution regulations were binding for Swedish pulp and paper plants even though average discharges were below the permitted levels. They demonstrated this by showing that average discharges were affected by the level of the regulation. (This project estimated this relationship for U.S. plants). Their analysis was motivated by an appeal to the randomness of discharges. However, they did not measure the randomness and therefore, were limited in the questions they could address.

Findings. The fundamental uncontrollability of discharges is necessary to understand this project's research findings. The research has focused on the role of randomness. This role is more important and more interesting than originally believed.

Permit-Compliance System (PCS) data were used to construct plant-level measures of discharge randomness (i.e., plant-level variances). A measure of randomness for each plant in the data set was constructed, and the investigator was careful to separate true randomness (uncontrollability) from other controllable factors that might be affecting discharges. Data are available for as many as 108 months from each plant; in the analysis, all plants with at least 15 months of data were included. These data were not necessarily consecutive. In the regressions, observations were weighted by the number of months of data used in constructing the randomness measure.

Some variability in plant discharges is not random and must be removed to measure the true random component. A Stewhart Cumulative Sum (CUSUM) test was used to identify plants whose discharges underwent a statistically significant structural change. Such structural changes could be due to capital changes in the plant or changes in plant management. This is not the kind of variability whose role this project seeks to understand. The set of 764 plants for which there was no structural change were analyzed. The hypothesis that plants with higher variability pollute less on average was tested and accepted. The size of this effect is large.

The investigator concludes, however, that randomness is not the sole reason for the low discharge levels. The evidence points to the likelihood that plants truly are overcomplying with their permits, not simply compensating for randomness. This project's analysis predicts that in the absence of discharge randomness, plants will pollute at about 60 percent of their permitted level.

Overcompliance appears to be primarily due to community pressure and, perhaps, a difficulty for managers/operators in gauging the "true" compliance when discharges are so variable. A high degree of randomness and its variability across plants imply that plants' polluting behavior cannot fully be understood based only on an analysis of average (or total) discharges. Therefore, a plant-specific compliance measure was constructed by dividing median compliance by its standard deviation, which can be converted into a predicted violation rate. This measure is shown to be quite close to observed violation rates. For each plant, the median compliance rate and the standard deviation were used to construct the predicted probability of a violation, using the assumption that discharges are distributed log normal. These predicted probabilities are quite low. The median probability of violation is less than 1 percent.

The effect of plant and community characteristics on the probability of a violation were examined. Community characteristics have large effects on plant behavior, although the effects are measured imprecisely. Small plants in poorer communities tend to have higher probabilities of violation.

The study results indicated that: (1) there is no statistical difference in the probability of violation between U.S. Environmental Protection Agency (EPA)- and state-regulated plants; (2) manufacturing plants have a significantly lower probability of violation than wastewater treatment plants; (3) large plants have a significantly lower probability of violation than small plants; and (4) community variables, including income and minority composition, have large effects on the probability of violation, but these effects are imprecisely measured. These results are conditional on the plant being in the sample (i.e., on not having undertaken a significant plant change during 1992-1999).

Policy Implications. There are implications for three regulatory issues: (1) current regulations, (2) tradeable permits, and (3) informal regulation.

Current Regulation. Because of randomness and community pressure, together and possibly separately, plants no longer exhibit a one-to-one relationship between discharges and permitted levels. This feature must be considered when predicting the effect of any change in the regulation. A move to make the regulations more stringent by reducing the permitted concentrations would not necessarily lead to an equal reduction in discharges.

Randomness also enhances the role of EPA's enforcement policy. A move to enforce standards over a shorter time (the daily limits) would likely reduce discharges further; a move to enforce standards over a longer time (annual average concentration or total annual quantity) would likely raise discharges. These predictions arise because of differences in the degree of randomness of these measures.

Tradeable Permits. Tradeable permits, a commonly proposed direction for pollution regulations, would likely, in the case of random water pollution discharges, raise overall pollution levels substantially. Currently, plants pollute well below their permitted levels on average, at least in part because of the risk of a random violation. If a fully tradeable, nonbankable pollution permit system were in place, plants could, in principle, pollute exactly at their permitted level. Whenever low discharges occurred, they would sell the excess permits; when high discharges occurred, they would buy permits from other plants. As long as randomness was uncorrelated across plants, they could pollute exactly at their permitted level on average and incur no net penalty. The difference between this and the current levels of discharges shows the consequence of tradeability.

Informal Regulation. The large role that I find for community pressure may lead policymakers to wonder whether the formal legal system of regulation could be modified or somehow "relaxed." There are two answers to this question. First, between 10 and 25 percent of the plants in the sample are not overcomplying, and some of them are rather seriously out of compliance. Community pressure is a feature primarily of well-to-do communities. This project's evidence, although preliminary, does not suggest that community pressure would be a successful regulatory tool in poorer or minority communities.

Second, the interaction between community pressure and formal regulation is unknown. Do plant managers, in trying to convince the community that they are doing a good job, point to their permitted levels as a benchmark against which their success can be measured? If so, are there alternative nonregulatory benchmarks that could play this role? Answers to these questions would seem to be required before greater reliance on "informal" regulation is prescribed. It is worth noting that community pressure's effects on local plants' pollution decisions under tradeable permits systems also are unknown.

Randomness creates an agency problem for engineers and plant managers; it makes it more difficult for them to show that they are doing a good job in properly balancing treatment costs with the penalty costs of violations. Excessively low probabilities of violation may be their optimal response in this situation. For regulators and the public, randomness means they must judge, from the highly variable discharge pattern, the abatement efforts being made by plants. Indeed, it seems possible that managers may not realize they are overcomplying to the degree that they are; their "true" compliance is masked by randomness, which makes inference difficult. The policy implications of this problem have not yet been fully explored.

Future Activities:
The main body of the research has been completed. There remain many interesting issues to be pursued. However, the goals of the original research project have largely been met. The final report for this grant will be prepared and submitted during the next reporting period.